A new hybrid algorithm of simulated annealing and simplex downhill for solving multiple-objective aggregate production planning on fuzzy environment

Aggregate production planning (APP) is a significant level that seeks efficient production systems. In actual condition, APP decisions, production inputs, and relevant planning parameters are intrinsically imprecise, which results in significant complexities in the generation of master production schedules. Thus, this paper proposes a hybridization of a fuzzy programming, simulated annealing (SA), and simplex downhill (SD) algorithm called fuzzy–SASD to establish multiple-objective linear programming models and consequently resolve APP problems in a fuzzy environment. The proposed strategy is dependent on Zimmerman’s approach for handling all inexact operating costs, data capacities, and demand variables. The SD algorithm is employed to balance exploitation and exploration in SA, thereby efficient and effective (speed and quality) solution for the APP model. The proposed approach produces rates for efficient solutions of APP in large-scale problems that are 33, 83, and 89% more efficient than those of particle swarm optimization (PSO), standard algorithm (SA), and genetic algorithm (GA), respectively. Moreover, the proposed approach produces a significantly low average rate for computational time at only 64, 77, and 24% compared with those of GA, PSO, and SA, respectively. Experimental results indicate that the fuzzy–SASD is the most effectual of all approaches.